Methods for detecting safety signals in clinical trials using groupings of adverse events by body-system or system organ class.

Dataset

Description

R package implementing a number of methods for detecting adverse events in clinical trials based on groupings of adverse events by body-system or system organ class. The methods include an implementation of the Three-Level Hierarchical model for Clinical Trial Adverse Event Incidence Data of Berry and Berry (2004) , an implementation of the same model without the Point Mass (Model 1a from Xia et al (2011)), and extended Bayesian hierarchical methods based on system organ class or body-system groupings for interim analyses. The package also implements a number of methods for error control when testing multiple hypotheses, specifically control of the False Discovey Rate (FDR). The FDR control methods implemented are the Benjamini-Hochberg procedure, the Double False Discovery Rate, the Group Benjamini-Hochberg and subset Benjamini-Hochberg methods. Also included are the Bonferroni correction and the unadjusted testing procedure.

External deposit with Zenodo, cite as:

Raymond Carragher. (2019, May 30). rcarragh/c212: Release 0.95 to CRAN (Version 0.95). Zenodo. http://doi.org/10.5281/zenodo.3235283
Date made available30 May 2019

Cite this

Carragher, R. B. (Creator). (30 May 2019). Methods for detecting safety signals in clinical trials using groupings of adverse events by body-system or system organ class.10.5281/zenodo.3235282